Method and apparatus for improving transmission of data on a bandwidth mismatched channel

ABSTRACT

A method and apparatus for providing transmission on a channel in a network are disclosed. For example, the method receives a plurality of source samples, divides the plurality of source samples into a plurality of subbands in accordance with a ratio of the plurality of source samples to a number of channel uses of the channel, wherein each subband comprises a first number of source samples, determines a channel input from the plurality of source samples in accordance with a hybrid coding scheme, and transmits the channel input over the network.

The present disclosure relates generally to data transmission on acommunication network and, more particularly, to a method and apparatusfor improving data transmission on a bandwidth mismatched channel in anetwork, e.g., a wireless network.

BACKGROUND

Digital coding schemes based on source-channel separation principle aresensitive to channel condition. If the channel condition is worse thanwhat a code is designed for, the decoder fails to completely decode acode that has traversed the channel. On the other hand, if the channelcondition is better than what the code is designed for, the code failsto provide any performance improvement. Thus, the digital coding schemesuffers from a threshold effect.

One approach is to design the coding scheme to achieve an optimalperformance for a given channel condition. However, the resulting codeoffers performance improvement either when the channel condition isworse or when the channel condition is better, but not both.

SUMMARY

In one embodiment, the present disclosure provides a method and anapparatus for providing transmission on a channel in a network, e.g., awireless network. For example, the method receives a plurality of sourcesamples, divides the plurality of source samples into a plurality ofsubbands in accordance with a ratio of the plurality of source samplesto a number of channel uses of the channel, wherein each subbandcomprises a first number of source samples, determines a channel inputfrom the plurality of source samples in accordance with a hybrid codingscheme, and transmits the channel input over the network.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an exemplary transmission system related to thepresent disclosure;

FIG. 2 illustrates a flow chart of a method for providing transmissionof data on a channel in a network;

FIG. 3 illustrates a flowchart of a method for determining a channelinput from source samples in accordance with a hybrid coding scheme; and

FIG. 4 illustrates a high level block diagram of a general purposecomputer suitable for use in performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure broadly describes a method and apparatus forimproving the transmission of data on a bandwidth mismatched channel ina network, e.g., a wireless network. Although the teachings of thepresent disclosure are discussed below in the context of wirelessnetworks, the teaching is not so limited. Namely, the teachings of thepresent disclosure can be applied for other types of wireless networks,etc.

Digital communication systems have been widely deployed in moderncommunication networks. However, these digital communications systemsare based on Shannon's source-channel separation principle. Digitalcoding schemes based on source-channel separation principle aresensitive to channel conditions. For example, a code may be designed fora specific channel condition. However, if the channel condition is worsethan what the code is designed for, the decoder may fail to completelydecode a code that has traversed the channel. In another scenario, ifthe channel condition is better than what the code is designed for, thecode may fail to provide any performance improvement. Thus, the digitalcoding scheme based on source-channel separation principle suffers froma threshold effect.

In one embodiment, the present disclosure provides a hybrid analog anddigital coding scheme that is capable of improving decoding performancewhen the channel condition is worse and when the channel condition isbetter, simultaneously. The hybrid analog and digital coding scheme isalso referred to as a hybrid coding scheme. Namely, the coding scheme ofthe present disclosure is able to achieve a performance improvement forboth the better and the worse channel conditions, while keeping theperformance optimal for a given target channel condition.

FIG. 1 illustrates an exemplary transmission system 100 related to thepresent disclosure. The transmission system 100 comprises a source 101(broadly a data source), an encoder 103, a transmission channel 105, adecoder 107, and a sink 109 (broadly a data destination). Thetransmission channel 105 traversed the network 110. The network 110 maybe a wireless network. It should be noted that each of the modulesdiscussed above in the transmission system 100 may be implemented in oneor more hardware devices, e.g., one or more hardware computing deviceshaving one or more processors and the like.

In one embodiment, the source 101 transmits samples (e.g., broadly datasamples) to the encoder 103. The encoder 103 performs the coding, i.e.,applying a coding to the received samples. The resulting (encoded)output of the encoder is then transmitted to the decoder 107 via thetransmission channel 105. The decoder 107 performs reconstruction of thesource samples and forwards the decoded samples to the sink 109. Itshould be noted that the transmission system 100 may comprise many othernetwork elements (not shown), e.g., transmitters, receivers, basestations, routers, switches, gateways, and the like.

The problem of providing transmission of data on a bandwidth mismatchedchannel may be formulated as a lossy source broadcast problem with twousers. In one embodiment, the source and the additive noise of thechannel are both Gaussian. The measure used to evaluate the performanceof a code is the reconstruction mean squared error. In order to moreclearly illustrate the problem, the source and channel conditions aremathematically defined as follows:

First, the source is a memoryless zero-mean Gaussian source with unitvariance S˜

(0,1). Similarly, the channel is a memoryless channel given by: Y=X+

, wherein X is the channel input subject to an average power constraintP, and

˜

(0, N) is a memoryless additive Gaussian noise. Then, the transmissionsignal to noise ratio (P/N) is denoted by SNR.

In order to clearly illustrate the teachings of the current disclosure,the concept of channel use is introduced. Channel use refers to achannel's ability to transmit a sample. For example, if there are tensource samples generated per second and there are thirty channel usesper second, then there are three channel uses available for transmittingeach source sample. If one channel use is available per source sample,the source bandwidth and the channel bandwidth are said to be matched.If the source bandwidth and the channel bandwidth are matched, a linearscaling of the source sample provides an optimal performance.

However, when the source bandwidth and the channel bandwidth are notmatched, there are no known methods that provide a completecharacterization for both worse and better channel conditions,simultaneously. The present disclosure provides a hybrid coding schemefor transmitting on a bandwidth mismatched channel that providesperformance improvement for both worse and better channel conditions.

First consider two other receivers—a receiver with a worse channelcondition and another receiver with a better channel condition. Thereceiver with a worse channel condition has a transmission signal tonoise ratio of SNR⁺ and the receiver with a better channel condition hasa transmission signal to noise ratio of SNR⁻. Hence, SNR⁺≧SNR andSNR⁻≦SNR. The channel output for the channel with the worse channelcondition is denoted by Y⁺. The channel output for the channel with thebetter channel condition is denoted by Y⁻.

For example, if there are three users and the users are designated as agood user, a median user and a bad user, the respective transmissionsignal to noise ratios for the three users may be designated by SNR⁺,SNR, and SNR⁻, respectively.

The bandwidth of the source (source bandwidth) and the bandwidth of thechannel (channel bandwidth) may not be matched. For example, for each msource samples, a total of n channel uses may be available. The ratio ofthe number of channel uses to the number of source samples is referredto as a bandwidth expansion factor. The bandwidth expansion factor isdefined as:

$b\overset{\Delta}{=}{\frac{n}{m}.}$If b<1, the bandwidth is said to be compressed. If b>1, the bandwidth issaid to be expanded. If b=1, the bandwidth is said to be matched.

Using Shannon's source channel separation theorem, while ignoring allother receivers, the optimal distortion for a receiver with transmissionsignal to noise ratio of SNR is given by:

${{D^{*}({SNR})} = {{D\left( {\frac{n}{m}C} \right)} = {D({bC})}}},$wherein D(R) is a Gaussian distortion rate function and C is a Gaussianchannel capacity per channel use. The Gaussian channel capacity perchannel use may also be referred to as simply as a Gaussian channelcapacity.

Then, substituting for the Gaussian distortion-rate function and theGaussian channel capacity, the optimal distortion (described above) maybe determined as follows: D*(SNR)=exp(−b log(1+SNR))=(1+SNR)^(−b),wherein the natural logarithm function (log) is used.

For the case in which the bandwidth is expanded (i. e. b>1), a methoddescribed by Reznic, Feder and Zamir may achieve an improved distortionbehavior for either a receiver with worse channel condition or betterchannel condition—but not both.

For example, for the channel conditions SNR⁻, SNR and SNR⁺,respectively, Reznic, Feder and Zamir may achieve either a distortiontriple (D^(†)(SNR⁻), D*(SNR), D*(SNR)) or a distortion triple (D′(SNR⁻),D*(SNR), D^(†)(SNR⁺)), wherein

${{D^{\dagger}\left( {SNR}^{+} \right)} = \frac{1}{\left( {1 + {SNR}} \right)^{b - 1}\left( {1 + {SNR}^{+}} \right)}},{{D^{\dagger}\left( {SNR}^{-} \right)} = \frac{1}{\left( {1 + {SNR}^{-}} \right)}},{and}$${D^{\prime}\left( {SNR}^{-} \right)} = {1 - {\frac{{SNR}^{-}}{\left( {1 + {SNR}} \right)^{b - 1}\left( {1 + {SNR}^{-}} \right)}.}}$

However, the Reznic, Feder and Zamir method does not achieve improveddistortion behavior for both worse and better channel conditions,simultaneously. Note that D^(†) has greater performance gain over D* ascompared to the performance gain of D′ over the D*. In order to achievethe D^(†)(SNR⁻) for the bad user, Reznic, Feder and Zamir cannot provideany performance gain for the good user over the performance of themedian user. Similarly, in order to achieve D^(†)(SNR⁺) for the gooduser, Reznic, Feder and Zamir can only achieve minimal distortionreduction for the bad user over the median user.

For the case in which the bandwidth is compressed (i. e, b <1), a methoddescribed by Prabhakaran may be used to achieve an improved distortionbehavior for either a receiver with worse channel condition or areceiver with a better channel condition—but not both.

For example, for the channel conditions SNR⁻, SNR and SNR⁺,respectively, Prabhakaran may achieve either a distortion triple(D^(†)(SNR⁻), D*(SNR), D*(SNR)) or a distortion triple (D′(SNR⁻),D*(SNR), D^(†)(SNR⁺)), wherein,

${{D^{\dagger}\left( {SNR}^{+} \right)} = {{\left( {1 - b} \right){D^{*}({SNR})}} + {b\frac{SNR}{{\left( {1 + {SNR}} \right)^{b}{SNR}^{+}} + {SNR} - {SNR}^{+}}}}},{{D^{\dagger}\left( {SNR}^{-} \right)} = {1 - {{bSNR}^{-}\frac{1 + {SNR} - \left( {1 + {SNR}} \right)^{1 - b}}{{SNR}\left( {1 + {SNR}^{-}} \right)}}}},{and}$${{D^{\prime}\left( {SNR}^{-} \right)} = {1 - {{bSNR}^{-}\frac{\left( {1 + {SNR}} \right)^{b} - 1}{{SNR}\left( {1 + {SNR}^{-}} \right)}}}},$but not both.

In both the Reznic, Feder and Zamir and the Prabhakaran approaches(described above), D′(SNR⁻)≧D^(†)(SNR⁻). A coding scheme used for theabove bandwidth expansion and bandwidth compression examples is a codingscheme designed for sending a Gaussian source on a Gaussian channel,wherein the bandwidth is matched (i.e. b=1), and a side information isavailable at the receiver but not at the transmitter. The coding schemeis referred to as a hybrid joint source channel Wyner-Ziv coding scheme.In order to use the hybrid joint source channel Wyner-Ziv coding scheme,first let S_(d) represent the side information, Z represent a Gaussianrandom variable independent of S_(d), and S represent the source. Then,S=S_(d)+Z . The coding scheme then operates as follows:

First, a set of codewords may be generated by an encoder according to adistribution U=X+KS, wherein X is a Gaussian random variable independentof the source S. The objective of the encoder is to find a codewordu^(m) such that the codeword is jointly typical with a source sequenceS^(m). The encoder then transmits u^(m)−κS^(m) on the channel. A decoderreceives a channel output Y^(m). A side information S_(d) ^(m) isavailable at the receiver. The decoder uses Y^(m), S_(d) ^(m) and jointtypicality for decoding. When the rate of the codebook is chosen suchthat I(U; S_(d), Y)>R>I(U; S), then the codeword is correctly decoded.The source estimate is formed using S_(d), U, Y jointly. In order toachieve an optimal performance for the channel condition of SNR, anappropriate value is chosen for κ.

It is important to note that other coding schemes may be used. Forexample, an encoder may use a distribution U=X+αT+κS, wherein T is achannel state. The encoder may then transmit the resulting X vector,u^(m)−αT^(m)−κS^(m), on the channel. If the rate of the generated Ucodebook is chosen such that I(U; Y)>R>I(U;T,S), the codeword iscorrectly decoded and the source estimate is formed by using Y, Ujointly.

The present disclosure provides a hybrid coding scheme that achieves thedistortion triple (D^(†)(SNR⁻), D*(SNR), D^(†)(SNR⁺)) for the channelconditions SNR⁻, SNR and SNR⁺, respectively, wherein the bandwidth ofthe channel is compressed.

For example, the bandwidth expansion factor,

${b\overset{\Delta}{=}\frac{n}{m}},$for the channel is such that b<1. Since the channel is compressed whenb<1, for convenience, another factor is herein defined as the inverse ofthe bandwidth expansion factor. The new factor is denoted by α. Then,

$a = {b^{- 1} = {\frac{m}{n}.}}$In one embodiment, the new factor (the inverse of the bandwidthexpansion factor for the channel) has an integer value.

In one embodiment, the coding scheme of the present disclosure is basedon a subband quantization. When the value of a is an integer greaterthan one, the method first breaks the source samples evenly into aportions, each portion having n source samples. Each of the resultingportions (having n source samples) is referred to as a subband of thesource samples, or simply as a subband. For example, if there are tensource samples and two channel uses, α is then equal to five. Then, themethod breaks the ten source samples into five subbands with each of thefive subbands having two source samples.

In one embodiment, the method then digitally quantizes the sourcesamples in each subband except the source samples in the last subband.For example, the source samples in each of the subbands 1, 2, . . . ,α−1 are digitally quantized using a Gaussian codebook. The sourcesamples in the last subband (subband α) are to be transmitted uncoded.Thus, the last subband is not quantized. For the example above, sourcesamples in subbands 1-4 are digitally quantized and source samples insubband 5 are kept for transmission through a network uncoded.

In one embodiment, the Gaussian codebooks used for quantizing thesubbands 1, 2, . . . , α−1 are generated independently. For example, theGaussian codebook used for quantizing source samples in a particularsubband is independent of a Gaussian codebook used for quantizing sourcesamples in any other subband.

In one embodiment, the quantizing for each subband that is quantized isperformed using a quantization rate denoted by R, wherein

$R = {\frac{b}{2}{{\log\left( {1 + {SNR}} \right)}.}}$Note mat me quantization rate R is not applicable for the last subband,since the last subband is not quantized.

In one embodiment, the method then scales the source samples that arequantized, for each source sample that is quantized. In one embodiment,the scaling for each source sample that is quantized is performed byallocating power to each subband a power level that supports thequantization rate R.

In one embodiment, the allocating power to each subband that supportsthe quantization rate R is performed by allocating a power level P_(i)for each subband i=1, 2, . . . , α−1 such that

${R = {\frac{b}{2}{\log\left( {1 + \frac{P_{i}}{{\sum\limits_{j = {i + 1}}^{a}P_{i}} + N}} \right)}}},$where N is the noise described above.

The method then scales the source samples for the last subband. Forexample, for subband a, the source samples are scaled to match the powerlevel of the channel.

The method then broadcasts the source samples that are quantized andscaled for subbands 1, 2, . . . , α−1, and the source samples that arescaled (without quantizing) for subband α. For example, the broadcastmay be performed by superimposing codewords. For example, the codewordsfor each subbands 1, 2, . . . , α−1 are codewords created by quantizingand scaling the source samples for the respective subband. The codewordsfor subband α are created by scaling the source samples for subband α.

As described above, allocating of the power level P_(i) for each of thesubbands i=1, 2, . . . , α−1 supports the quantization rate R for eachof the subbands 1, 2, . . . , α−1. Note that the quantization rate R issupported if the distortion goal is met. In order to describe thedistortion the particular power allocations, the distortion triple(D^(†)(SNR⁻), D*(SNR), D^(†)(SNR⁺)) for the channel conditions SNR⁻, SNRand SNR⁺, respectively, are then analyzed.

For a median and a good user, the allocation of the power level P_(i) isequivalent to a power allocation for a unified coding scheme for hybridtransmission of a Gaussian source over a Gaussian channel, wherein theGaussian channel favors the good user while simultaneously providing anoptimal performance for the median user. For the good user, the methodachieves the distortion D^(†)(SNR⁺). For the median user, the methodachieves the distortion D*(SNR).

For the bad user, the channel quality of SNR⁻ is not sufficient for thecodewords of the bad user to be decoded reliably. However, for eachsubband, the method of the present disclosure is able to extractinformation to estimate the source sample. If the source sample for eachsubband is estimated as

(S|Y⁻), the distortion for each subband i=1, 2, . . . , α−1 is given by:

$\begin{matrix}{D_{i} = {{D^{*}({SNR})} + {\left\lbrack {1 - \frac{P_{i}}{{Var}\left( Y^{-} \right)}} \right\rbrack\left( {1 - {D^{*}({SNR})}} \right)}}} \\{= {1 - \frac{P_{i}}{{Var}\left( Y^{-} \right)} + {\frac{P_{i}}{{Var}\left( Y^{-} \right)}{{D^{*}({SNR})}.}}}}\end{matrix}$

For the last subband (subband number α)

$D_{a} = {1 - {\frac{P_{a}}{{Var}\left( Y^{-} \right)}.}}$

The overall distortion is then determined as the summation of thedistortions of all the subbands. Thus,

${D\left( {SNR}^{-} \right)} = {\frac{1}{a}{\sum\limits_{i = 1}^{a}\;{D_{i}.}}}$

Substituting the above values for each subband yields:

$\begin{matrix}{{D\left( {SNR}^{-} \right)} = {1 - {b\frac{{SNR}^{-}}{1 + {SNR}^{-}}} + {b\frac{P - P_{a}}{{Var}\left( Y^{-} \right)}{D^{*}({SNR})}}}} \\{= {1 - {{b\left( {SNR}^{-} \right)}\frac{1 + {SNR} - \left( {1 + {SNR}} \right)^{({1 - b})}}{{SNR}\left( {1 + {SNR}^{-}} \right)}} + {b\frac{P - P_{a}}{{Var}\left( Y^{-} \right)}{D^{*}({SNR})}}}} \\{= {{D^{\dagger}\left( {SNR}^{-} \right)}.}}\end{matrix}$

Thus, the distortion triple (D^(†)(SNR⁻), D*(SNR), D^(†)(SNR⁺)) for thechannel conditions SNR⁻, SNR and SNR⁺, respectively, is achieved. Hence,the quantization rate R is supported by the allocation of power asdescribed above.

FIG. 2 illustrates a flow chart of a method 200 for providingtransmission on a channel in a network, wherein a ratio of a number ofsource samples to a number of channel uses of the channel is an integervalue, e.g., greater than one. In one embodiment, one or more steps ofmethod 200 are implemented by an encoder used in a transmission system,e.g., a transmission system used in a wireless network. Method 200starts in step 205 and proceeds to step 210.

In step 210, method 200 receives a plurality of source samples. Forexample, method 200 receives m source samples to be encoded andtransmitted across the network.

In step 213, method 200 breaks or divides the plurality of sourcesamples (or the source vector) into a plurality of subbands, whereineach subband comprises a first number of source samples. In oneembodiment, the first number of source samples is equal to the number ofchannel uses. For example, the method breaks the number of sourcesamples described above into α subbands, with each of the α subbandscomprising n channel uses. For example, if there are ten source samplesbeing received for encoding per second and there are two channel usesavailable per second, then m=10, n=2 and α=5. Then, the ten sourcesamples received in each second are evenly divided into 5 subbands, witheach subband comprising 2 source samples. The method then proceeds todetermine the channel input for the channel uses.

In step 217, method 200 determines a channel input from the plurality ofsource samples in accordance with a hybrid coding scheme. For example,if m source samples are received and there are n channel uses, the msource samples are divided into a plurality of subbands, wherein thenumber of the plurality of subbands is equal to the ratio of m to n. Theratio of m to n is equal to α, as described above. Then, for each of thesubbands 1, 2, . . . , α−1, the method quantizes and scales the sourcesamples that are received. For subband α, the method scales the sourcesamples that are received. The resulting source samples are superimposedto determine the channel input for broadcasting on the channel uses ofthe channel. For the example above, the codewords generated from eachthe five subbands are superimposed to form the channel input. In oneembodiment, FIG. 3 illustrates a flowchart of a method for determiningof the channel input from source samples that are received in accordancewith the hybrid coding scheme.

In step 219, method 200 transmits the channel input that is determinedover the network. For example, the channel input is determined andtransmitted towards a decoder over the network. The decoder may thenreconstruct the source sample. For example, the decoder may use a linearmean square estimation technique for reconstructing the analog sourcesample. The method either proceeds to step 210 to continue receivingsource samples or to step 222 to end the current process.

FIG. 3 illustrates a flowchart of a method 300 for determining of achannel input from the plurality of source samples in accordance withthe hybrid coding scheme. For example, the method 300 may be used instep 217 of FIG. 2 to determine the channel input by superimposingcodewords generated from the plurality of source samples of eachsubband, as in a broadcast channel. Method 300 starts in step 303 andproceeds to step 325.

In step 325, method 300 identifies a subband for each source sample ofthe plurality of source samples. For example, if there are ten sourcesamples received and there are five subbands, the first two sourcesamples are placed in the first subband, the next two samples are placedin the second subband, and so on.

In step 330, method 300 obtains a particular source sample of theplurality of source samples for which a subband is identified. Forexample, a particular source sample is obtained such that the sourcesample is processed. For example, the method obtains a particular sourcesample of the plurality of source samples to be processed.

In step 335, method 300 determines if the particular source sample thatis obtained is in a last subband. If the source sample is in the lastsubband, the method proceeds to step 370. Otherwise, the method proceedsto step 340. For the example above, if the particular source sample isin one of the subbands 1, 2, . . . , α−1, the method proceeds to step340. If the particular source sample is in subband α, the methodproceeds to step 370.

In step 340, method 300 quantizes the particular source sample. In oneembodiment, the source sample is quantized using a Gaussian codebook, orbroadly a codebook. For example, the method quantizes an analog sourcesample to obtain a digital quantized source sample using a Gaussiancodebook.

In step 345, method 300 scales the particular source sample that isquantized. In one embodiment, the scaling of the source sample that isquantized is performed by allocating power to a particular subband ithat supports a quantization rate R for the particular subband i, suchthat the rate is

${R = {\frac{b}{2}{\log\left( {1 + \frac{P_{i}}{{\sum\limits_{j = {i + 1}}^{a}P_{i}} + N}} \right)}}},$where N is a noise of a channel on which the source sample is to betransmitted. The method then proceeds to step 385.

In step 370, method 300 scales the particular source sample of theplurality of source samples such that a power level of the subbandmatches a power level of the channel on which the source sample is to betransmitted. For example, a received (analog) source sample is scaledwithout quantization. The method then proceeds to step 385.

In step 385, method 300 determines if there is at least one sourcesample that has not been processed. If there is at least one sourcesample that has not been processed, the method proceeds to step 330.Otherwise, the method proceeds to step 390.

In step 390, method 300 forms a channel input. For example, the channelinput is formed by superimposing a plurality of codewords (by quantizingand scaling for subbands 1, 2, . . . , α−1, or only scaling for subbandα) of the plurality of source samples. For the example above, thechannel input is formed by superimposing the codewords of the fivesubbands, with appropriate power allocated to each subband.

In one example, for a source sample in the last subband, the processinggenerates a scaled version of the source sample. In another example, fora source sample that is in a particular subband other than the lastsubband, the processing generates a codeword from the source sample byquantizing via a Gaussian codebook (digitizing) and scaling the powerallocated to the codeword in order to support the quantization rate ofthe particular subband. The codewords are then superimposed to form thechannel input. The channel input may then be transmitted over thenetwork, as described in step 219 of FIG.2. The method either proceedsto step 303 to continue determining channel inputs by processing sourcesamples or to step 395 to end the current process.

Note that in one embodiment, an encoder and a decoder are used at bothlocations. For example, the transmission system of FIG. 1 may be abidirectional transmission system. In one embodiment, the encoder anddecoder may be integrated in one device.

It should be noted that although not explicitly specified, one or moresteps of the methods 200 or 300 described herein may include a storing,displaying and/or outputting step as required for a particularapplication. In other words, any data, records, fields, and/orintermediate results discussed in the method can be stored, displayed,and/or outputted to another device as required for a particularapplication. Furthermore, steps or blocks in FIG. 2 or FIG. 3 thatrecite a determining operation, or involve a decision, do notnecessarily require that both branches of the determining operation bepracticed. In other words, one of the branches of the determiningoperation can be deemed as an optional step.

FIG. 4 depicts a high level block diagram of a general purpose computersuitable for use in performing the functions described herein. Asdepicted in FIG. 4, the system 400 comprises a hardware processorelement 402 (e.g., a CPU), a memory 404, e.g., random access memory(RAM) and/or read only memory (ROM), a module 405 for providingtransmission of data on a bandwidth mismatched channel, and variousinput/output devices 406 (e.g., storage devices, including but notlimited to, a tape drive, a floppy drive, a hard disk drive or a compactdisk drive, a receiver, a transmitter, a speaker, a display, a speechsynthesizer, an output port, and a user input device (such as akeyboard, a keypad, a mouse, and the like)).

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a general purposecomputer or any other hardware equivalents, e.g., computer readableinstructions pertaining to the method(s) discussed above can be used toconfigure a hardware processor to perform the steps of the abovedisclosed method. In one embodiment, the present module or process 405for providing transmission of data on a bandwidth mismatched channel canbe loaded into memory 404 and executed by processor 402 to implement thefunctions as discussed above. As such, the present process 405 forproviding transmission on a bandwidth mismatched channel (includingassociated data structures) of the present disclosure can be stored on anon-transitory (e.g., tangible and physical) computer readable storagemedium, e.g., RAM memory, magnetic or optical drive or diskette and thelike. For example, the processor 402 can be programmed or configuredwith instructions (e.g., computer readable instructions) to perform thesteps of methods 200 and 300.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A method for transmitting data on a channel in anetwork, comprising: receiving, by a processor, a plurality of sourcesamples; dividing, by the processor, the plurality of source samplesinto a plurality of subbands in accordance with a ratio of the pluralityof source samples to a number of channel uses of the channel, whereineach subband comprises a first number of source samples, wherein thenumber of channel uses refers to an ability of the channel to transmitthe plurality of source samples; determining, by the processor, achannel input from the plurality of source samples in accordance with ahybrid coding scheme; and transmitting, by the processor, the channelinput over the network to a decoder that reconstructs the plurality ofsource samples using the channel input for each of the plurality ofsubbands regardless of whether a channel condition of a respectivesubband of the plurality of subbands is worse or better than a targetchannel condition.
 2. The method of claim 1, wherein the first number ofsource samples is equal to a number of channel uses.
 3. The method ofclaim 1, wherein the determining the channel input from the plurality ofsource samples comprises: identifying a subband for a particular sourcesample of the plurality of source samples.
 4. The method of claim 3,wherein the determining the channel input from the plurality of sourcesamples further comprises: determining when the particular source sampleis for a last subband of the plurality of subbands.
 5. The method ofclaim 4, further comprising: scaling the particular source sample, whenthe particular source sample is in the last subband of the plurality ofsubbands.
 6. The method of claim 5, wherein the scaling of theparticular source sample is performed such that a power level of thelast subband of the plurality of subbands matches a power level of thechannel.
 7. The method of claim 4, further comprising: quantizing theparticular source sample, when the particular source sample is not inthe last subband of the plurality of subbands; and scaling theparticular source sample that is quantized.
 8. The method of claim 7,wherein the quantizing is performed using a codebook.
 9. The method ofclaim 7, wherein the scaling of the particular source sample that isquantized is performed by allocating a power level to the subband thatis identified for the particular source sample, wherein the power levelsupports a quantization rate, wherein the quantization rate is used forquantizing the particular source sample.
 10. A non-transitorycomputer-readable medium storing a plurality of instructions, which whenexecuted by a processor, cause the processor to perform operations fortransmitting data on a channel in a network, the operations comprising:receiving a plurality of source samples; dividing the plurality ofsource samples into a plurality of subbands in accordance with a ratioof the plurality of source samples to a number of channel uses of thechannel, wherein each subband comprises a first number of sourcesamples, wherein the number of channel uses refers to an ability of thechannel to transmit the plurality of source samples; determining achannel input from the plurality of source samples in accordance with ahybrid coding scheme; and transmitting the channel input over thenetwork to a decoder that reconstructs the plurality of source samplesusing the channel input for each of the plurality of subbands regardlessof whether a channel condition of a respective subband of the pluralityof subbands is worse or better than a target channel condition.
 11. Thenon-transitory computer-readable medium of claim 10, wherein the firstnumber of source samples is equal to a number of channel uses.
 12. Thenon-transitory computer-readable medium of claim 10, wherein thedetermining the channel input from the plurality of source samplescomprises: identifying a subband for a particular source sample of theplurality of source samples.
 13. The non-transitory computer-readablemedium of claim 12, wherein the determining the channel input from theplurality of source samples further comprises: determining when theparticular source sample is for a last subband of the plurality ofsubbands.
 14. The non-transitory computer-readable medium of claim 13,further comprising: scaling the particular source sample, when theparticular source sample is in the last subband of the plurality ofsubbands.
 15. The non-transitory computer-readable medium of claim 14,wherein the scaling of the particular source sample is performed suchthat a power level of the last subband of the plurality of subbandsmatches a power level of the channel.
 16. The non-transitorycomputer-readable medium of claim 13, further comprising: quantizing theparticular source sample, when the particular source sample is not inthe last subband of the plurality of subbands; and scaling theparticular source sample that is quantized.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the quantizing isperformed using a codebook.
 18. The non-transitory computer-readablemedium of claim 16, wherein the scaling of the particular source samplethat is quantized is performed by allocating a power level to thesubband that is identified for the particular source sample, wherein thepower level supports a quantization rate, wherein the quantization rateis used for quantizing the particular source sample.
 19. An apparatusfor providing transmission on a channel in a network, comprising: aprocessor; and a computer-readable medium storing a plurality ofinstructions which, when executed by the processor, cause the processorto perform operations, the operations comprising: receiving a pluralityof source samples; dividing the plurality of source samples into aplurality of subbands in accordance with a ratio of the plurality ofsource samples to a number of channel uses of the channel, wherein eachsubband comprises a first number of source samples, wherein the numberof channel uses refers to an ability of the channel to transmit theplurality of source samples; determining a channel input from theplurality of source samples in accordance with a hybrid coding scheme;and transmitting the channel input over the network to a decoder thatreconstructs the plurality of source samples using the channel input foreach of the plurality of subbands regardless of whether a channelcondition of a respective subband of the plurality of subbands is worseor better than a target channel condition.
 20. The apparatus of claim19, wherein the first number of source samples is equal to a number ofchannel uses.